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Journal articles on the topic 'Pre-trained convolutional neural networks'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Wang, Yufei, and Garrison Cottrell. "Recognizing Urban Tribes with pre-trained Convolutional Neural Networks." Journal of Vision 15, no. 12 (2015): 1171. http://dx.doi.org/10.1167/15.12.1171.

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Karim, Mokdad, Koushavand Behrang, and Boisvert Jeff. "Automatic variogram inference using pre-trained Convolutional Neural Networks." Applied Computing and Geosciences 25 (February 2025): 100219. https://doi.org/10.1016/j.acags.2025.100219.

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Thirumaladevi, Satharajupalli, Satharajupalli Thirumaladevi, and Sailaja Maruvada. "Competent scene classification using feature fusion of pre-trained convolutional neural networks." TELKOMNIKA 21, no. 04 (2023): 805–14. https://doi.org/10.12928/telkomnika.v21i4.24463.

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In view of the fact that the development of convolutional neural networks (CNN) and other deep learning techniques, scientists have become more interested in the scene categorization of remotely acquired images as well as other algorithms and datasets. The spatial geometric detail information may be lost as the convolution layer thickness increases, which would have a significant impact on the classification accuracy. Fusion-based techniques, which are regarded to be a viable way to express scene features, have recently attracted a lot of interest as a solution to this issue. Here, we suggeste

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Jadhav, Sachin B. "Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 4 (2019): 328. http://dx.doi.org/10.11591/ijai.v8.i4.pp328-341.

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<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast

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Sachin, B. Jadhav, R. Udupi Vishwanath, and B. Patil Sanjay. "Convolutional neural networks for leaf image-based plant disease classification." International Journal of Artificial Intelligence (IJ-AI) 8, no. 4 (2019): 328–41. https://doi.org/10.11591/ijai.v8.i4.pp328-341.

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Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system imp

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Towpunwong, Nattakan, and Napa Sae-Bae. "Dog Breed Classification and Identification Using Convolutional Neural Networks." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 17, no. 4 (2023): 554–63. http://dx.doi.org/10.37936/ecti-cit.2023174.253728.

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This study aimed to assess the effectiveness of using pre-trained models to extract biometric information, specifically the dog breed and dog identity, from images of dogs. The study employed pre-trained models to extract feature vectors from the dog images. Multi-Layer Perceptron (MLP) models then used these vectors as input to train dog breed and identity classifiers. The dog breeds used in this study comprised two Thai breeds, Bangkaew and Ridgeback, and 120 foreign breeds. For dog breed classification, the results showed that, among the ImageNet classification models, the pre-trained NasNe

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Omran, Eman M., Randa F. Soliman, Ayman A. Eisa, Nabil A. Ismail, and Fathi E. Abd El-Samie. "Cancelable Iris Recognition System with Pre-trained Convolutional Neural Networks." Menoufia Journal of Electronic Engineering Research 28, no. 1 (2019): 95–101. http://dx.doi.org/10.21608/mjeer.2019.76778.

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Puneet Gupta. "Pneumonia Detection Using Convolutional Neural Networks." January 2021 7, no. 01 (2021): 77–80. http://dx.doi.org/10.46501/ijmtst070117.

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Abstract— Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia, need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolution

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Dudekula, Usen, and Purnachand N. "Linear fusion approach to convolutional neural networks for facial emotion recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1489. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1489-1500.

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Facial expression recognition is a challenging problem in the scientific field of computer vision. Several face expression recognition (FER) algorithms are proposed in the field of machine learning, and deep learning to extract expression knowledge from facial representations. Even though numerous algorithms have been examined, several issues like lighting changes, rotations and occlusions. We present an efficient approach to enhance recognition accuracy in this study, advocates transfer learning to fine-tune the parameters of the pre-trained model (VGG19 model ) and non-pre-trained model conv

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Tsarev, Andrey, and Sergey Namestnikov. "Diagnosis of pneumonia using convolutional neural networks." Bulletin of Ulyanovsk State Technical Univercity 106, no. 2 (2024): 43–46. http://dx.doi.org/10.61527/1684-7016-2024-2-43-46.

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The problem of the accuracy of the diagnosis of diseases is considered, for the solution it is proposed to use deep learning methods, namely, to develop a neural network for the diagnosis of pneumonia from Xrays. Two approaches were used in the solution: the compilation of a self-developed network and the use of pre-trained models as the basis for creating custom image classifiers

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T. Blessington, Dr Praveen, and Prof Ravindra Mule. "Image Forgery Detection Based on Parallel Convolutional Neural Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28428.

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Abstract— Due to the availability of deep networks, progress has been made in the field of image recognition. Images and videos are spreading very conveniently and with the availability of strong editing tools the tampering of digital content become easy. To detect such scams, we proposed techniques. In our paper, we proposed two important aspects of employing deep convolutional neural networks to image forgery detection. We first explore and examine different preprocessing method along with convolutional neural networks (CNN) architecture. Later we evaluated the different transfer learning fo

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Dakdareh, Sara Ghasemi, and Karim Abbasian. "Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Using Convolutional Neural Networks." Journal of Alzheimer's Disease Reports 8, no. 1 (2024): 317–28. http://dx.doi.org/10.3233/adr-230118.

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Background: Alzheimer’s disease and mild cognitive impairment are common diseases in the elderly, affecting more than 50 million people worldwide in 2020. Early diagnosis is crucial for managing these diseases, but their complexity poses a challenge. Convolutional neural networks have shown promise in accurate diagnosis. Objective: The main objective of this research is to diagnose Alzheimer’s disease and mild cognitive impairment in healthy individuals using convolutional neural networks. Methods: This study utilized three different convolutional neural network models, two of which were pre-t

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Mishra, Gangeshwar, Prinima Gupta, and Rohit Tanwar. "Target Recognition Using Pre-Trained Convolutional Neural Networks and Transfer Learning." Procedia Computer Science 235 (2024): 1445–54. http://dx.doi.org/10.1016/j.procs.2024.04.136.

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Kim, Jun-Hwa, and Chee Sun Won. "Action Recognition in Videos Using Pre-Trained 2D Convolutional Neural Networks." IEEE Access 8 (2020): 60179–88. http://dx.doi.org/10.1109/access.2020.2983427.

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Lopes, U. K., and J. F. Valiati. "Pre-trained convolutional neural networks as feature extractors for tuberculosis detection." Computers in Biology and Medicine 89 (October 2017): 135–43. http://dx.doi.org/10.1016/j.compbiomed.2017.08.001.

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Khaleel, Maha Ibrahim, and Amir Lakizadeh. "Skin cancer diagnosis using hybrid deep pre-trained convolutional neural networks." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 3 (2025): 2291. https://doi.org/10.11591/ijai.v14.i3.pp2291-2301.

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<span lang="EN-US">As a variant of skin cancer, melanoma represents a substantial menace to the health and overall well-being of individuals. Statistics reveal that 55% of skin cancer patients succumb to this particular disease. However, early detection plays a crucial role in reducing mortality rates and saving lives. In the past several decades, there has been a rise in the adoption of deep learning algorithms, capturing the interest of researchers working in this field. One popular method involves utilizing pre-trained deep neural networks. In this study, a hybrid approach is employed

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Ji, Qingge, Jie Huang, Wenjie He, and Yankui Sun. "Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images." Algorithms 12, no. 3 (2019): 51. http://dx.doi.org/10.3390/a12030051.

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Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets. We propose a strategy to modify DNNs, which improves their performance on retinal optical coherence tomography (OCT) images. Deep features of pre-trained DNN are high-level features of natural images. These features harm the training of transfer learning. Our strategy is to remove some deep convolutional layers of the state-of-the-art pre-trained networks: GoogLeNet, ResNet and DenseNet. We try to find

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Mannem, Revanth Reddy, and Suraj Bhyri. "Breast Cancer Detection Based on Convolutional Neural Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 837–41. http://dx.doi.org/10.22214/ijraset.2023.55262.

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Abstract: Breast cancer remains a critical health concern, demanding early detection and accurate classification for effective treatment. In this research, we conduct a comparative study between a custom-designed Convolutional Neural Network (CNN) and the pre-trained DenseNet121 model for breast cancer detection and classification. We begin by curating a comprehensive dataset of breast cancer images and apply appropriate data preprocessing techniques for optimal model input. The dataset is divided into training, validation, and testing sets to evaluate model performance. The CNN model is const

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Zhu, Zhaotong, and Youfeng Hu. "Sonar image recognition based on fine-tuned convolutional neural network." MATEC Web of Conferences 283 (2019): 04012. http://dx.doi.org/10.1051/matecconf/201928304012.

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To solve the problem of sonar image recognition, a sonar image recognition method based on fine-tuned Convolutional Neural Network (CNN) is proposed in this paper. With the development of deep learning, CNN shows impressive performance in image recognition. However, massive data is needed to train a CNN from beginning. Through fine-tuning pre-trained CNN can help us training CNN from relatively high starting points, based on those pre-trained CNNs, only few data is needed to retrain a CNN which focus on sonar image recognition. A scaled model experiment shows that based on the architecture of

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Dudekula, Usen, and Purnachand N. "Linear fusion approach to convolutional neural networks for facial emotion recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1489–500. https://doi.org/10.11591/ijeecs.v25.i3.pp1489-1500.

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Facial expression recognition is a challenging problem in the scientific field of computer vision. Several face expression recognition (FER) algorithms are proposed in the field of machine learning, and deep learning to extract expression knowledge from facial representations. Even though numerous algorithms have been examined, several issues like lighting changes, rotations and occlusions. We present an efficient approach to enhance recognition accuracy in this study, advocates transfer learning to fine-tune the parameters of the pre-trained model (VGG19 model) and non-pre-trained model convo

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Suhendar, H., V. Efelina, and M. Ziveria. "Fruit Quality Classification using Convolutional Neural Network." Journal of Physics: Conference Series 2377, no. 1 (2022): 012015. http://dx.doi.org/10.1088/1742-6596/2377/1/012015.

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Fruit quality identification is very important in the food industry for maintaining product quality. The quality control in the food industry commonly conducted by human senses which is lack of objectivity and takes long time for real-time mass production quality control. The quality of the fruit can be identified through its color, smell, and texture. This study uses fruit image to classify the quality of the fruit. We trained artificial neural networks for classifying fruit quality from Indian Fruit Dataset with Quality (FruitNet). The dataset contains six classes of fruits with three catego

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22

Sarabu, Ashok, and Ajit Kumar Santra. "Human Action Recognition in Videos using Convolution Long Short-Term Memory Network with Spatio-Temporal Networks." Emerging Science Journal 5, no. 1 (2021): 25–33. http://dx.doi.org/10.28991/esj-2021-01254.

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Two-stream convolutional networks plays an essential role as a powerful feature extractor in human action recognition in videos. Recent studies have shown the importance of two-stream Convolutional Neural Networks (CNN) to recognize human action recognition. Recurrent Neural Networks (RNN) has achieved the best performance in video activity recognition combining CNN. Encouraged by CNN's results with RNN, we present a two-stream network with two CNNs and Convolution Long-Short Term Memory (CLSTM). First, we extricate Spatio-temporal features using two CNNs using pre-trained ImageNet models. Sec

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Escudero, Cristian A., Andrés F. Calvo, and Arley Bejarano. "Black Sigatoka Classification Using Convolutional Neural Networks." International Journal of Machine Learning and Computing 11, no. 4 (2021): 323–26. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1055.

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In this paper we present a methodology for the automatic recognition of black Sigatoka in commercial banana crops. This method uses a LeNet convolutional neural network to detect the progress of infection by the disease in different regions of a leaf image; using this information, we trained a decision tree in order to classify the level of infection severity. The methodology was validated with an annotated database, which was built in the process of this work and which can be compared with other state-of-the-art alternatives. The results show that the method is robust against atypical values

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Chen, Mingang, Wenjie Chen, Wei Chen, Lizhi Cai, and Gang Chai. "Skin Cancer Classification with Deep Convolutional Neural Networks." Journal of Medical Imaging and Health Informatics 10, no. 7 (2020): 1707–13. http://dx.doi.org/10.1166/jmihi.2020.3078.

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Skin cancers are one of the most common cancers in the world. Early detections and treatments of skin cancers can greatly improve the survival rates of patients. In this paper, a skin lesions classification system is developed with deep convolutional neural networks of ResNet50, which may help dermatologists to recognize skin cancers earlier. We utilize the ResNet50 as a pre-trained model. Then, by transfer learning, it is trained on our skin lesions dataset. Image preprocessing and dataset balancing methods are used to increase the accuracy of the classification model. In classification of sk

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Masud, Mehedi, M. Shamim Hossain, Hesham Alhumyani, et al. "Pre-Trained Convolutional Neural Networks for Breast Cancer Detection Using Ultrasound Images." ACM Transactions on Internet Technology 21, no. 4 (2021): 1–17. http://dx.doi.org/10.1145/3418355.

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Volunteer computing based data processing is a new trend in healthcare applications. Researchers are now leveraging volunteer computing power to train deep learning networks consisting of billions of parameters. Breast cancer is the second most common cause of death in women among cancers. The early detection of cancer may diminish the death risk of patients. Since the diagnosis of breast cancer manually takes lengthy time and there is a scarcity of detection systems, development of an automatic diagnosis system is needed for early detection of cancer. Machine learning models are now widely us

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Akash, Chaudhary, AnkitaSingh, and Km.Yachana. "Anti Spoofing Face Detection with Convolutional Neural Networks Classifier." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 745–50. https://doi.org/10.5281/zenodo.7953326.

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The ability to detect spoofed faces has become a critical concern in various applications, such as face recognition systems, banking, and security measures. Thisresearchpresentsa simple system that can detect whether a facein video stream is spoofed or real using pre-trained models for face detection and anti-spoofing. The system uses a continuous loop to read each frame of the video stream, to assess whether a face image is real or spoof, first detect faces using the pre-trained face detection model, then crop and resize the face image. If the model predicts that the face is fake, the system

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Vivien, L. Beyala, and J. Nkenlifack Marcellin. "EXTENDED CONVOLUTIONAL NEURAL NETWORKS POST-TRAINED WITH FACTORED STATISTICAL MACHINE." International Research Journal of Computer Science VII, no. VII (2020): 197–208. https://doi.org/10.26562/irjcs.2020.v0707.003.

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This paper investigate the potential of coupling two machine translation research approaches while taking full advantage of each method, namely, the deterministic (neuronal) and probabilistic (statistical) approaches, in order to address three main problems occurring in MT, that is, language pairs having grammatical structure and word order that differs drastically, data sparseness and the number of out of vocabulary (OOV) words generated. Additionally, we integrated word-level linguistic features (Part-of- Speech with compounds, lemmatization and/or word class) so as to decrease the number of

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Jain, Himani. "Images Spam Detection on Online Social Media using CNN with Pre-Trained Model." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 2427–38. http://dx.doi.org/10.22214/ijraset.2024.63490.

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Abstract: Nowadays attackers move to image spam techniques instead of text based. The Majority of conventional techniques solely possess the capability to identify spam confined to textual content and hyperlinks. In this research we have done “Deep Convolutional Neural Networks” (DCNNs)in conjunction with pre-trained architectures. Image classification is one of the areas that has increased in the last decade very rapidly. But due to the less computational resources it become very challenging to train a good image classification model. With the help of Transfer learning, we can overcome this t

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Nurtay, M., M. Kissina, A. Tau, A. Akhmetov, G. Alina, and N. Mutovina. "Brain tumor classification using deep convolutional neural networks." Computer Optics 49, no. 2 (2025): 253–62. https://doi.org/10.18287/2412-6179-co-1476.

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This study presents a comparative analysis of various convolutional neural network (CNN) models for brain tumor detection on MRI medical images. The primary aim was to assess the effectiveness of different CNN architectures in accurately identifying brain tumors. Multiple models were trained, including a custom-designed CNN with its specific layer architecture, and models based on Transfer Learning utilizing pre-trained neural networks: ResNet-50, VGG-16, and Xception. Performance evaluation of each model in terms of accuracy metrics such as precision, recall, F1-score, and confusion matrix on

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Ashqar, Belal A. M., and Samy S. Abu-Naser. "Identifying Images of Invasive Hydrangea Using Pre-Trained Deep Convolutional Neural Networks." International Journal of Control and Automation 12, no. 4 (2019): 15–28. http://dx.doi.org/10.33832/ijca.2019.12.4.02.

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Satharajupalli, Thirumaladevi, Kilari Veera Swamy, and Maruvada Sailaja. "Competent scene classification using feature fusion of pre-trained convolutional neural networks." TELKOMNIKA (Telecommunication Computing Electronics and Control) 21, no. 4 (2023): 805. http://dx.doi.org/10.12928/telkomnika.v21i4.24463.

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Tarwani, Harit, Shivang Patel, and Parth Goel. "Deep learning approach for weather classification using pre-trained convolutional neural networks." Procedia Computer Science 252 (2025): 136–45. https://doi.org/10.1016/j.procs.2024.12.015.

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Fernandes, Marília Parreira, Adriano Carvalho Costa, Heyde Francielle do Carmo França, et al. "Convolutional Neural Networks in the Inspection of Serrasalmids (Characiformes) Fingerlings." Animals 14, no. 4 (2024): 606. http://dx.doi.org/10.3390/ani14040606.

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Aquaculture produces more than 122 million tons of fish globally. Among the several economically important species are the Serrasalmidae, which are valued for their nutritional and sensory characteristics. To meet the growing demand, there is a need for automation and accuracy of processes, at a lower cost. Convolutional neural networks (CNNs) are a viable alternative for automation, reducing human intervention, work time, errors, and production costs. Therefore, the objective of this work is to evaluate the efficacy of convolutional neural networks (CNNs) in counting round fish fingerlings (S

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Buehler, C., F. Schenkel, W. Gross, G. Schaab, and W. Middelmann. "STRATEGIC OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORKS FOR HYPERSPECTRAL LAND COVER CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 363–69. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-363-2020.

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Abstract. Hyperspectral data recorded by future earth observation satellites will have up to hundreds of narrow bands that cover a wide range of the electromagnetic spectrum. The spatial resolution (around 30 meters) of such data, however, can impede the integration of the spatial domain for a classification due to spectrally mixed pixels and blurred edges in the data. Hence, the ability of performing a meaningful classification only relying on spectral information is important. In this study, a model for the spectral classification of hyperspectral data is derived by strategically optimizing

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Eduardo, Adriany A. F., Gustavo A. S. Martinez, Ted W. Grant, Lucas B. S. Da Silva, and Wei-Liang Qian. "Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks." J 8, no. 2 (2025): 15. https://doi.org/10.3390/j8020015.

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The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. Specifically, this study explores how pre-trained neural networks with appropriate architecture can be exploited to predict apparently distinct but internally related features. Tentative predictions are made by observing only an insignificant cropped fraction of the material’s profile. The neural network models are trained a

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Liao, Siyu, and Bo Yuan. "CircConv: A Structured Convolution with Low Complexity." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4287–94. http://dx.doi.org/10.1609/aaai.v33i01.33014287.

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Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications. However, the large model sizes of DNNs yield high demands on computation resource and weight storage, thereby limiting the practical deployment of DNNs. To overcome these limitations, this paper proposes to impose the circulant structure to the construction of convolutional layers, and hence leads to circulant convolutional layers (CircConvs) and circulant CNNs. The circulant structure and models can be either trained from scratch o

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Ge, Qiang, Fengxue Ruan, Baojun Qiao, Qian Zhang, Xianyu Zuo, and Lanxue Dang. "Side-Scan Sonar Image Classification Based on Style Transfer and Pre-Trained Convolutional Neural Networks." Electronics 10, no. 15 (2021): 1823. http://dx.doi.org/10.3390/electronics10151823.

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Side-scan sonar is widely used in underwater rescue and the detection of undersea targets, such as shipwrecks, aircraft crashes, etc. Automatic object classification plays an important role in the rescue process to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in side-scan sonar images is still lacking, which is due to a lack of datasets and the small number of image samples containing specific target objects. Secondly, the real data of side-scan sonar images are unbalanced. Therefore, a side-scan sonar

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Tannouche, Adil, Ahmed Gaga, Mohammed Boutalline, and Soufiane Belhouideg. "Weeds detection efficiency through different convolutional neural networks technology." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 1048. http://dx.doi.org/10.11591/ijece.v12i1.pp1048-1055.

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The preservation of the environment has become a priority and a subject that is receiving more and more attention. This is particularly important in the field of precision agriculture, where pesticide and herbicide use has become more controlled. In this study, we propose to evaluate the ability of the deep learning (DL) and convolutional neural network (CNNs) technology to detect weeds in several types of crops using a perspective and proximity images to enable localized and ultra-localized herbicide spraying in the region of Beni Mellal in Morocco. We studied the detection of weeds through s

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Long, Yahui, Min Wu, Yong Liu, et al. "Pre-training graph neural networks for link prediction in biomedical networks." Bioinformatics 38, no. 8 (2022): 2254–62. http://dx.doi.org/10.1093/bioinformatics/btac100.

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Abstract Motivation Graphs or networks are widely utilized to model the interactions between different entities (e.g. proteins, drugs, etc.) for biomedical applications. Predicting potential interactions/links in biomedical networks is important for understanding the pathological mechanisms of various complex human diseases, as well as screening compound targets for drug discovery. Graph neural networks (GNNs) have been utilized for link prediction in various biomedical networks, which rely on the node features extracted from different data sources, e.g. sequence, structure and network data. H

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R, Niranjana. "Sign Language Recognition Using Convolutional Neural Network." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31370.

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This project introduces a real-time sign language detection system powered by Convolutional Neural Networks (CNNs), designed to aid individuals with hearing impairments in communication. Leveraging OpenCV for video capture and hand detection, along with custom modules for hand tracking and image classification, the system seamlessly integrates deep learning methodologies. A pre-trained CNN model, trained on a comprehensive dataset of sign language gestures, forms the core of the system, ensuring accurate classification in real-time. By capturing video frames from a webcam, detecting hands, and

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Šanca, Simon, Krištof Oštir, and Alen Mangafić. "Building detection with convolutional networks trained with transfer learning." Geodetski vestnik 64, no. 04 (2021): 559–93. http://dx.doi.org/10.15292/geodetski-vestnik.2021.04.559-593.

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Building footprint detection based on orthophotos can be used to update the building cadastre. In recent years deep learning methods using convolutional neural networks have been increasingly used around the world. We present an example of automatic building classification using our datasets made of colour near-infrared orthophotos (NIR-R-G) and colour orthophotos (R-G-B). Building detection using pretrained weights from two large scale datasets Microsoft Common Objects in Context (MS COCO) and ImageNet was performed and tested. We applied the Mask Region Convolutional Neural Network (Mask R-C

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Tian, Feng, Shiao Zhang, Miao Cao, and Xiaojun Huang. "Research on accelerated coding absorber design with deep learning." Physica Scripta 98, no. 9 (2023): 096003. http://dx.doi.org/10.1088/1402-4896/acf00a.

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Abstract The traditional design of metamaterials requires a large amount of prior knowledge in electromagnetism and is time-consuming and labour-intensive, but these challenges can be addressed by using trained neural networks to accelerate the forward design process. However, when it comes to coded absorbers, there is no clear ‘guidance manual’ on which neural network is most effective for this task. In this paper, three basic neural networks (full connection, one-dimensional convolution and two-dimensional convolution) are designed considering the apparent pattern and structural parameters o

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Кonarev, D., and А. Gulamov. "ACCURACY IMPROVING OF PRE-TRAINED NEURAL NETWORKS BY FINE TUNING." EurasianUnionScientists 5, no. 1(82) (2021): 26–28. http://dx.doi.org/10.31618/esu.2413-9335.2021.5.82.1231.

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Methods of accuracy improving of pre-trained networks are discussed. Images of ships are input data for the networks. Networks are built and trained using Keras and TensorFlow machine learning libraries. Fine tuning of previously trained convoluted artificial neural networks for pattern recognition tasks is described. Fine tuning of VGG16 and VGG19 networks are done by using Keras Applications. The accuracy of VGG16 network with finetuning of the last convolution unit increased from 94.38% to 95.21%. An increase is only 0.83%. The accuracy of VGG19 network with fine-tuning of the last convolut

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Samma, Hussein Salem Ali, and Bader Lahasan. "Convolutional Neural Network for Skull Recognition." International Journal of Innovative Computing 12, no. 1 (2021): 55–58. http://dx.doi.org/10.11113/ijic.v12n1.347.

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Automatic skull identification systems play a vital role for forensic law authorities to recognize victim identity. Motivated by potential applications of these kinds of systems, this research aims to apply a pre-trained deep convolutional neural network (CNN) for face skull recognition. Basically, the unknown skull image is fed to a pre-trained CNN network to extract a 1D feature vector, and then it will be matched with photos at database agencies to identify the closest match. To validate the proposed skull recognition system, it has been applied for a total of 13 skulls, and the reported re

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Gaskarov, Rodion Dmitrievich, Alexey Mikhailovich Biryukov, Alexey Fedorovich Nikonov, Daniil Vladislavovich Agniashvili, and Danil Aydarovich Khayrislamov. "Steel Defects Analysis Using CNN (Convolutional Neural Networks)." Russian Digital Libraries Journal 23, no. 6 (2020): 1155–71. http://dx.doi.org/10.26907/1562-5419-2020-23-6-1155-1171.

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Steel is one of the most important bulk materials these days. It is used almost everywhere - from medicine to industry. Detecting this material's defects is one of the most challenging problems for industries worldwide. This process is also manual and time-consuming. Through this study we tried to automate this process. A convolutional neural network model UNet was used for this task for more accurate segmentation with less training image data set for our model. The essence of this NN (neural network) is in step-by-step convolution of every image (encoding) and then stretching them to initial

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Nawaf, Asmaa Yaseen, and Wesam M. Jasim. "A pre-trained model vs dedicated convolution neural networks for emotion recognition." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 1123. http://dx.doi.org/10.11591/ijece.v13i1.pp1123-1133.

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Facial expression recognition (FER) is one of the most important methods influencing human-machine interaction (HMI). In this paper, a comparison was made between two models, a model that was built from scratch and trained on FER dataset only, and a model previously trained on a data set containing various images, which is the VGG16 model, then the model was reset and trained using FER dataset. The FER+ data set was augmented to be used in training phases using the two proposed models. The models will be evaluated (extra validation) by using images from the internet in order to find the best m

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Asmaa, Yaseen Nawaf, and M. Jasim Wesam. "A pre-trained model vs dedicated convolution neural networks for emotion recognition." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 1123–33. https://doi.org/10.11591/ijece.v13i1.pp1123-1133.

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Facial expression recognition (FER) is one of the most important methods influencing human-machine interaction (HMI). In this paper, a comparison was made between two models, a model that was built from scratch and trained on FER dataset only, and a model previously trained on a data set containing various images, which is the VGG16 model, then the model was reset and trained using FER dataset. The FER+ data set was augmented to be used in training phases using the two proposed models. The models will be evaluated (extra validation) by using images from the internet in order to find the best m

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Yin, Zhenyu, Zisong Wang, Chao Fan, Xiaohui Wang, and Tong Qiu. "Edge Detection via Fusion Difference Convolution." Sensors 23, no. 15 (2023): 6883. http://dx.doi.org/10.3390/s23156883.

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Edge detection is a crucial step in many computer vision tasks, and in recent years, models based on deep convolutional neural networks (CNNs) have achieved human-level performance in edge detection. However, we have observed that CNN-based methods rely on pre-trained backbone networks and generate edge images with unwanted background details. We propose four new fusion difference convolution (FDC) structures that integrate traditional gradient operators into modern CNNs. At the same time, we have also added a channel spatial attention module (CSAM) and an up-sampling module (US). These struct

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Raza, Rehan, Fatima Zulfiqar, Shehroz Tariq, Gull Bano Anwar, Allah Bux Sargano, and Zulfiqar Habib. "Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks." Mathematics 10, no. 1 (2021): 26. http://dx.doi.org/10.3390/math10010026.

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Human skin is the most exposed part of the human body that needs constant protection and care from heat, light, dust, and direct exposure to other harmful radiation, such as UV rays. Skin cancer is one of the dangerous diseases found in humans. Melanoma is a form of skin cancer that begins in the cells (melanocytes) that control the pigment in human skin. Early detection and diagnosis of skin cancer, such as melanoma, is necessary to reduce the death rate due to skin cancer. In this paper, the classification of acral lentiginous melanoma, a type of melanoma with benign nevi, is being carried o

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Yang, Zhaochen. "Enhancing Convolutional Neural Networks via separately trained kernels for digit recognition." Applied and Computational Engineering 54, no. 1 (2024): 254–57. http://dx.doi.org/10.54254/2755-2721/54/20241659.

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This paper introduces a novel methodology for pre-training neural networks. Instead of the traditional approach of running a single classifier for all objects, the method used in the research constructs individual classifiers for each object to extract unique features. These classifiers, in essence, act as a series of binary classifiers, each pre-training a distinct set of convolutional kernels. These individually trained kernels are then combined and processed by a comprehensive classifier. The methodology leverages the power of individual feature extraction and collaborative processing to en

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